In many telecommunication network architectures a given set of client nodes must be served by different kinds of facility, which provide di fferent services and have diff erent capabilities. Such facilities must be located and dimensioned in the design phase. We tackle a particular location problem in which two sets of facilities, mid level and high level, … Read more
The single row facility layout problem (SRFLP) is an NP-hard combinatorial optimization problem that is concerned with the arrangement of n departments of given lengths on a line so as to minimize the weighted sum of the distances between department pairs. (SRFLP) is the one-dimensional version of the facility layout problem that seeks to arrange … Read more
We consider multiclass classification problems where the set of labels are organized hierarchically as a category tree. We associate each node in the tree with a classifier and classify the examples recursively from the root to the leaves. We propose a hierarchical Support Vector Machine (SVM) that encourages the classifier at each node of the … Read more
We analyze globally convergent derivative-free trust region algorithms relying on radial basis function interpolation models. Our results extend the recent work of Conn, Scheinberg, and Vicente to fully linear models that have a nonlinear term. We characterize the types of radial basis functions that fit in our analysis and thus show global convergence to first-order … Read more
This paper develops Jellyfish, an algorithm for solving data-processing problems with matrix-valued decision variables regularized to have low rank. Particular examples of problems solvable by Jellyfish include matrix completion problems and least-squares problems regularized by the nuclear norm or the max-norm. Jellyfish implements a projected incremental gradient method with a biased, random ordering of the … Read more
Given a family of real or complex monic polynomials of fixed degree with one affine constraint on their coefficients, consider the problem of minimizing the root radius (largest modulus of the roots) or root abscissa (largest real part of the roots). We give constructive methods for efficiently computing the globally optimal value as well as … Read more
This paper proposes efficient algorithms for group sparse optimization with mixed L21-regularization, which arises from the reconstruction of group sparse signals in compressive sensing, and the group Lasso problem in statistics and machine learning. It is known that encoding the group information in addition to sparsity will lead to better signal recovery/feature selection. The L21-regularization … Read more
The electrical vehicle energy management can be expressed as a Bang-Bang optimal control problem. In this work, we discuss on a new formulation and about the way to approximate this optimal control problem of Bang-Bang type via a discretization technique associated with a Branch-and-Bound algorithm. The problem that we focus on, is the minimization of … Read more
The electrical vehicle energy management can be expressed as a Bang-Bang optimal control problem. In this work, we discuss on a new formulation and about the way to approximate this optimal control problem of Bang-Bang type via a discretization technique associated with a Branch-and-Bound algorithm. The problem that we focus on, is the minimization of … Read more
Gaussian processes are the cornerstone of statistical analysis in many application ar- eas. Nevertheless, most of the applications are limited by their need to use the Cholesky factorization in the computation of the likelihood. In this work, we present a matrix-free approach for comput- ing the solution of the maximum likelihood problem involving Gaussian processes. … Read more